Rule-based fraud detection (RBFD) of transactions can be data intensive and/or computationally intensive, especially in a system that receives a high volume of transactions. For example, many payment systems require processing transactions involving hundreds to thousands of active merchants in a relatively short time. RBFD can involve either computing the number of transactions in a particular time interval in real time or maintaining a large amount of data. In addition, RBFD often requires maintaining transaction volume data representing the number of transactions that occur during different time intervals (e.g., transactions received in the last hour, last day, and the last month).